# NVIDIA Technical Blog News and tutorials for developers, data scientists, and IT admins This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## How to Use AI Agents to Prepare 3D Scenes for Simulation DevFeed: [How to Use AI Agents to Prepare 3D Scenes for Simulation]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-09-16T23:20:33Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Artificial Intelligence](), [Simulation](), [Physical AI](), [Simulation and Design](), [blender](), [Isaac Sim](), [Omniverse](), [Robotics Simulation](), [Robotics]() Tags: [3d](), [agentic](), [agentic-ai-generative-ai](), [agents](), [ai](), [blender](), [gpt-6-astra](), [how-to](), [isaac-sim](), [nemoclaw](), [nvidia](), [omniverse](), [openai](), [openusd](), [physical-ai](), [robotics](), [robotics-simulation](), [simulation](), [simulation-modeling-design]() ### AI overview This tutorial describes an agentic workflow for preparing Blender 3D scenes for robotics simulation. It covers scene inspection, OpenUSD metadata, physics properties, rendering preflight views, and validation for simulation-ready handoff to NVIDIA Isaac Sim or Isaac Lab. ### Source excerpt Agentic AI workflows can be used to prepare and validate digital twins for physical AI systems. Agents can inspect 3D scenes, author simulation-relevant data in... ## TensorRT Edge-LLM Completes the MLPerf Edge Agentic Benchmark 6.4x Faster on Jetson AGX Thor DevFeed: [TensorRT Edge-LLM Completes the MLPerf Edge Agentic Benchmark 6.4x Faster on Jetson AGX Thor]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-09-16T20:37:07Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Jetson AGX Thor Developer Kit](), [Inference Performance](), [benchmarking](), [TensorRT](), [Large Language Model](), [quantization](), [Artificial Intelligence]() Tags: [agentic-ai-generative-ai](), [ai-agent](), [ai-inference](), [evaluation](), [jetson](), [jetson-agx-thor-developer-kit](), [llm](), [llm-benchmarking](), [mlperf](), [nvidia](), [performance](), [quantization](), [tensorrt](), [tensorrt-llm](), [thor]() ### AI overview This article reports that NVIDIA TensorRT Edge-LLM ran Qwen3.6-27B on a single NVIDIA Jetson AGX Thor Developer Kit for the MLPerf Inference v6.1 Edge Agentic benchmark. Using NVFP4 quantization, tree-based multi-token prediction, and KV cache reuse, it achieved 52.33 tokens per second and completed 1,007 turns in 24 minutes and 36 seconds, 6.4 times faster than the llama.cpp reference submission. ### Source excerpt AI agents are moving from cloud data centers to vehicles, robots, and other edge devices. Unlike a chatbot that answers a single prompt, an agent works through... ## Translating CUDA Tile Operations from Python to Rust Using Agentic AI DevFeed: [Translating CUDA Tile Operations from Python to Rust Using Agentic AI]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-09-16T16:28:59Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [CUDA Tile](), [Rust](), [Agent Skill](), [Artificial Intelligence](), [AI Agent](), [GPU](), [benchmarking](), [Compiler]() Tags: [agent-skill](), [agentic-ai](), [agentic-ai-generative-ai](), [ai-agent](), [benchmarking](), [cuda](), [cuda-tile](), [cutile](), [developer-tools-techniques](), [gpu](), [mixture-of-experts-moe](), [python](), [rust]() ### AI overview This NVIDIA developer article explains a multi-agent workflow for translating cuTile Python and Triton-TileIR GPU kernels into cuTile Rust. The team ported 24 public TileGym operators, covering about 40 kernels, and achieved 99.5% of cuTile Python performance on average, with correctness and performance checks at each stage. ### Source excerpt cuTile Rust (cutile-rs) is a tile-based system for safe, idiomatic GPU kernel authoring in the Rust programming language. Extending the Rust ownership model to... ## Dense vs. MoE Models: Active Parameters, Throughput, and When to Choose Each DevFeed: [Dense vs. MoE Models: Active Parameters, Throughput, and When to Choose Each]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-09-15T17:00:11Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Mixture of Experts (MoE)](), [Nemotron](), [Routing (disambiguation)]() Tags: [agentic-ai-generative-ai](), [ai-inference](), [architecture](), [cost](), [data-center-cloud](), [deployment](), [llms](), [memory](), [mixture-of-experts-moe](), [models](), [moe](), [nemo](), [nemotron](), [performance](), [router](), [routing](), [tokens]() ### AI overview This article explains how dense and Mixture-of-Experts models activate parameters, compares their effects on throughput, memory cost, and serving complexity, and discusses when each architecture fits different deployment constraints. It uses Nemotron 3.5 Lightning as an example of an MoE model. ### Source excerpt How can a 30B-parameter model activate only 3B parameters per token, and still use the capacity of the larger model? Nemotron 3.5 Lightning illustrates the... ## How NVIDIA Groq 3 LPX Deterministic Execution Drives Power-Efficient High-Interactivity Inference on NVIDIA Vera Rubin DevFeed: [How NVIDIA Groq 3 LPX Deterministic Execution Drives Power-Efficient High-Interactivity Inference on NVIDIA Vera Rubin]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-09-15T16:55:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Groq 3 LPX](), [LPX](), [NVIDIA Vera Rubin](), [Vera Rubin NVL72](), [groq](), [Inference](), [Low Latency](), [long-context]() Tags: [agentic-ai-generative-ai](), [ai-factory](), [ai-inference](), [ai-platform](), [drive](), [dsx](), [groq](), [groq-3-lpx](), [inference](), [inference-performance](), [latency](), [low-latency](), [lpx](), [nvidia-vera-rubin](), [nvl72](), [performance](), [power-management](), [vera-rubin](), [vera-rubin-nvl72]() ### AI overview This NVIDIA developer article explains how Groq 3 LPX uses deterministic execution across 256 LPU chips to support low-latency inference on NVIDIA Vera Rubin. It describes compiler-scheduled execution and power-management techniques including Preemptive Power and Clock Period Synthesis. ### Source excerpt Power is a defining constraint for AI factories. As AI workloads demand a full compute platform to serve them, each component of that platform must maximize... ## How NVIDIA NVLink 6 Delivers Multi-Layer Resiliency for AI Factories DevFeed: [How NVIDIA NVLink 6 Delivers Multi-Layer Resiliency for AI Factories]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-09-15T16:55:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [NVLink](), [Nvidia](), [AI Factory](), [AI Infrastructure](), [Hardware]() Tags: [agentic-ai-generative-ai](), [ai](), [ai-factory](), [ai-infrastructure](), [data-center-cloud](), [hardware](), [industry](), [networking](), [networking-communications](), [nvidia](), [nvlink](), [resiliency](), [vera-rubin]() ### AI overview The article describes how NVIDIA NVLink 6 supports resiliency in large-scale AI factories. It explains that Vera Rubin NVL72 connects 72 Rubin GPUs into a single scale-up domain and outlines a multilayer approach using lossless networking, error correction, retry, flow control, and error containment to support continuous training and inference operations. ### Source excerpt For operators of large-scale AI factories, maximizing continuous output is essential for productivity. In massive-scale AI training, every GPU in the cluster... ## Scaling Federated Learning Across Docker, Kubernetes, and Slurm with NVIDIA FLARE DevFeed: [Scaling Federated Learning Across Docker, Kubernetes, and Slurm with NVIDIA FLARE]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-09-15T15:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Federated Learning](), [Docker](), [Kubernetes](), [Nvidia](), [Server](), [dataset](), [Deployment]() Tags: [architecture](), [compute](), [configuration](), [container](), [data-analytics-processing](), [data-science](), [dataset](), [deployment](), [docker](), [docker-container](), [federated-learning](), [gpu](), [job](), [kubernetes](), [nvidia](), [nvidia-flare](), [server]() ### AI overview This article explains how NVIDIA FLARE scales federated learning across sites with different infrastructure, including Docker, Kubernetes, and Slurm. Its two-layer architecture separates persistent federation services from on-demand job execution, while allowing each site to retain local control over compute, data, secrets, and scheduling. ### Source excerpt Federated learning (FL) projects often begin with a straightforward setup: one server, a few clients, and one dataset at each site. As those projects grow, the... ## Accelerating Dropless MoE Training in JAX with NVIDIA Transformer Engine DevFeed: [Accelerating Dropless MoE Training in JAX with NVIDIA Transformer Engine]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-09-14T16:39:15Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Mixture of Experts (MoE)](), [Artificial Intelligence](), [GPU](), [Large Language Model](), [Nvidia](), [Routing (disambiguation)](), [GB200](), [Network](), [Python](), [deepseek](), [mixtral](), [qwen]() Tags: [agentic-ai-generative-ai](), [ai](), [communication](), [deepseek](), [developer-tools-techniques](), [gb200](), [gpu](), [mixtral](), [mixture-of-experts-moe](), [mlops](), [moe](), [networks](), [nvfp4](), [nvidia](), [performance](), [python](), [qwen](), [router](), [routing](), [tensors](), [token](), [tokens](), [training-ai-models]() ### AI overview This article explains how NVIDIA Transformer Engine and JAX kernel optimizations accelerate dropless Mixture of Experts (MoE) training. It describes bottlenecks from token routing, expert dispatch and gathering, all-to-all communication, and ragged expert matrix operations. In DeepSeek-V3 training on NVIDIA GB200, the optimized approach increased performance from 103 to 1,068 TFLOPS per GPU, a 10.4x improvement. ### Source excerpt Mixture of experts (MoE) has become one of the defining architectural trends in large-scale AI model training. DeepSeek, Qwen, and Mixtral are examples of MoE... ## How Full-Stack NIM Optimizations Deliver 2.5x More Users on Nemotron 3 Ultra DevFeed: [How Full-Stack NIM Optimizations Deliver 2.5x More Users on Nemotron 3 Ultra]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-09-10T16:55:32Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Inference Performance](), [Language models]() Tags: [agentic-ai-generative-ai](), [benchmark](), [build-ai-agents](), [cache](), [developer-tools-techniques](), [gpu](), [inference](), [inference-performance](), [latency](), [mamba](), [mixture-of-experts](), [nemotron](), [nim](), [nvidia-blackwell](), [optimization]() ### AI overview The article explains how NVIDIA NIM bundles serving optimizations to improve throughput for Nemotron 3 Ultra while meeting latency targets on GPU infrastructure. ### Source excerpt Deploying a large language model is only the first step toward production-ready serving. Production teams also need to serve as many concurrent users as... ## High-Throughput Structure Prediction with BioNeMo Inference Runtime DevFeed: [High-Throughput Structure Prediction with BioNeMo Inference Runtime]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-09-10T15:00:00Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [AI, ML & Data Engineering]() Tags: [bionemo](), [cuda](), [cuda-graphs](), [data-center-cloud](), [data-science](), [drug-discovery](), [gpu](), [healthcare-life-sciences](), [how-to](), [hpc-scientific-computing](), [inference](), [integration](), [kernels](), [model](), [models](), [node](), [nvidia](), [python](), [pytorch](), [resource](), [scale](), [simulation-modeling-design](), [tokenization](), [torch](), [tutorial](), [workflow]() ### AI overview A tutorial on using NVIDIA BioNeMo Inference Runtime to accelerate biomolecular structure-prediction models on GPUs. It covers the end-to-end Boltz2 workflow, PyTorch integration, input requirements, and Ray-based single-node throughput scaling. ### Source excerpt Biomolecular structure prediction is now often run at proteome scale, where the goal is to move an entire worklist through the pipeline efficiently. NVIDIA... ## From Wafer-Out to First Token: Codifying Supply Chain Expertise with Nemotron and Palantir Foundry DevFeed: [From Wafer-Out to First Token: Codifying Supply Chain Expertise with Nemotron and Palantir Foundry]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-09-10T09:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [datacenter](), [networking](), [Software](), [data]() Tags: [agentic-ai-generative-ai](), [ai-agent](), [blackwell](), [compute](), [cuopt](), [data-center](), [gb200](), [infrastructure](), [llms](), [manufacturing](), [memory](), [nemo](), [nemotron](), [networking](), [nvidia](), [nvidia-blackwell](), [nvl72](), [performance](), [production](), [real-time](), [software](), [supply-chain](), [vera-rubin]() ### AI overview NVIDIA describes how it measures and reduces the time from wafer-out to first token across complex Grace Blackwell and Vera Rubin supply chains. The article focuses on time-to-rack, critical material allocation, real-time visibility, redundancy, reliability, and codifying human expertise. ### Source excerpt NVIDIA has one of the largest and most complex supply chains in the world, and its performance is measured from wafer-out to first token. The interval is in two... ## When to Use Encode-Prefill-Decode Disaggregation to Accelerate Multimodal Model Serving DevFeed: [When to Use Encode-Prefill-Decode Disaggregation to Accelerate Multimodal Model Serving]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-09-09T20:31:04Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Optimization](), [model-deployment](), [multimodal](), [Dynamo](), [Inference Performance](), [Embeddings](), [Mixture of Experts (MoE)](), [GPU](), [Open Source](), [Artificial Intelligence]() Tags: [agentic-ai-generative-ai](), [ai-agent](), [ai-inference](), [computer-vision-video-analytics](), [developer-tools-techniques](), [dynamo](), [embeddings](), [gpu](), [inference](), [low-latency-inference](), [mixture-of-experts-moe](), [model-serving](), [multimodal](), [nvfp4](), [open-source](), [optimization]() ### AI overview This article explains when encode-prefill-decode disaggregation improves multimodal model serving. Using NVIDIA Dynamo, it separates vision encoding from LLM prefill and decode so the stages can batch, schedule, and scale independently. The approach is most effective for image-heavy prompts, short-to-medium outputs, and quantized mixture-of-experts models, with reported gains of up to 5x in time to first token and 7x in end-to-end response time. ### Source excerpt Encode-prefill-decode (EPD) disaggregation is an inference optimization technique for multimodal models that separates the vision encoder stage from the prefill... ## CUDA Toolkit 13.4 Adds Windows on Arm Support and Greater Control over Shared GPUs DevFeed: [CUDA Toolkit 13.4 Adds Windows on Arm Support and Greater Control over Shared GPUs]() Original publisher: [Read original article]() Author: Jonathan Bentz Published: 2026-09-09T20:24:12Z Content type: release Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [CUDA](), [GPU](), [NVLink](), [Orchestration]() Tags: [cli](), [cuda](), [cuda-tile](), [developer-tools-techniques](), [gpu](), [nsight-tools-compute](), [nvidia](), [nvlink](), [release](), [simulation-modeling-design](), [top-stories](), [windows]() ### AI overview CUDA Toolkit 13.4 adds Windows on Arm support, preview support for the NVIDIA Rubin GPU architecture, and new GPU-sharing controls through MPS V3. It also introduces CUDA Compute Fabric Transport for data movement across NVIDIA NVLink fabric. ### Source excerpt Every NVIDIA CUDA Toolkit release adds functionality and performance improvements that help developers get more from NVIDIA GPUs and the broader NVIDIA software... ## Introducing CUDA Rust: Two Tracks for Writing GPU Kernels DevFeed: [Introducing CUDA Rust: Two Tracks for Writing GPU Kernels]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-09-08T12:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [GPU](), [Code generation](), [toolchain](), [C++]() Tags: [c-plus-plus](), [cuda](), [cuda-tile](), [data-science](), [developer-tools-techniques](), [dynamo](), [gpu](), [kernels](), [nemo-retriever](), [open-source](), [programming-languages-compilers](), [rust](), [toolchains]() ### AI overview NVIDIA introduces CUDA Rust for writing GPU kernels natively in Rust and compiling them to PTX. The article explains the SIMT and Tile programming tracks, recommends Tile as the default, and describes cuda-oxide's Rust-based code-generation path. ### Source excerpt In September 2026, NVIDIA announced it is leaning into native GPU programming in Rust. CUDA C++ and CUDA Python are mature, enterprise-grade toolchains, and... ## Building a Memory-Driven Agent with NVIDIA NemoClaw DevFeed: [Building a Memory-Driven Agent with NVIDIA NemoClaw]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-09-04T18:04:55Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Architecture & Design](), [Authorization](), [Markdown]() Tags: [agentic-ai-generative-ai](), [ai-agent](), [build-ai-agents](), [developer-tools-techniques](), [enterprise](), [llms](), [memory](), [nemoclaw](), [openshell](), [retrieval](), [retrieval-augmented-generation-rag](), [security]() ### AI overview This tutorial describes building a memory-driven AI agent with NVIDIA NemoClaw for enterprise work. It presents a structured self model, separates evidence from derived knowledge and governed execution, and emphasizes retrieval, user corrections, security, and authorization. ### Source excerpt Enterprise work spans messages, decisions, projects, and obligations that change over time. An AI agent that starts without this context must reconstruct it... ## Frontier Reasoning Reaches the Edge: How to Deploy and Optimize Models on NVIDIA Jetson DevFeed: [Frontier Reasoning Reaches the Edge: How to Deploy and Optimize Models on NVIDIA Jetson]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-09-04T16:21:04Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Jetson](), [model-deployment](), [Inference](), [Inference Performance](), [Optimization](), [Mixture of Experts (MoE)]() Tags: [agentic-ai-generative-ai](), [edge](), [edge-computing](), [how-to](), [inference](), [inference-performance](), [jetpack](), [jetson](), [jetson-orin](), [mixture-of-experts-moe](), [nvfp4](), [optimization](), [physical-ai](), [reasoning](), [thor](), [tutorial](), [vllm]() ### AI overview A tutorial on deploying and optimizing compact reasoning and agentic AI models on NVIDIA Jetson. It covers choosing models, improving inference with NVFP4 quantization and speculative decoding, serving example models with vLLM, and validating a configuration for a workload. ### Source excerpt Running reasoning and agentic AI at the edge has been harder than it needs to be. Until recently, models capable of multi-step reasoning were too large to run... ## How to Carry User Identity Across Federated Kubernetes and AI Platforms DevFeed: [How to Carry User Identity Across Federated Kubernetes and AI Platforms]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-09-03T22:36:02Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [AI Platform](), [Kubernetes](), [AI, ML & Data Engineering]() Tags: [ai-platform](), [ai-platforms-deployment](), [api](), [aws](), [cloud](), [cloud-services](), [data](), [data-center-cloud](), [developer-tools-techniques](), [how-to](), [identity](), [infrastructure](), [kubernetes](), [software-defined-data-center]() ### AI overview The article presents a central identity-gateway pattern for carrying user identity across federated Kubernetes, data, and AI platforms. It uses OIDC, a shared session store, stateless data-plane gateways, and an identity-validation API to establish trusted local identity context without distributing raw tokens to every application. ### Source excerpt Modern AI platforms are no longer a single application behind one login screen. A user may start in a central portal, open a governed dataset, launch a notebook... ## NVIDIA PAIR Virtual Inference Router Expands Available Compute on Your Local Network DevFeed: [NVIDIA PAIR Virtual Inference Router Expands Available Compute on Your Local Network]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-09-03T16:00:00Z Content type: release Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Inference](), [Local AI](), [GPU](), [Hardware]() Tags: [ai-agent](), [ai-agents](), [ai-inference](), [compute](), [content-creation-rendering](), [dgx-spark](), [edge-computing](), [gaming](), [geforce](), [gpu](), [hardware](), [inference](), [local](), [nvidia](), [open-source]() ### AI overview NVIDIA PAIR is a beta virtual inference router that distributes independent local inference requests across eligible machines on a home network. It works through compatible Ollama and LM Studio interfaces without requiring changes to an agent harness. ### Source excerpt AI agents are learning to do more by working together. A lead agent can break a complex task into smaller jobs and assign those jobs to specialized subagents.... ## The Modern CUDA Toolbox in Practice: A Step-by-Step Optimization Walkthrough DevFeed: [The Modern CUDA Toolbox in Practice: A Step-by-Step Optimization Walkthrough]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-09-02T17:15:57Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [CUDA](), [Optimization](), [GPU](), [toolchain](), [debug](), [Instrumentation](), [Algorithms]() Tags: [algorithms](), [benchmark](), [cuda](), [debug](), [developer-tools-techniques](), [gpu](), [how-to](), [image-processing](), [instrumentation](), [memory](), [optimization]() ### AI overview A step-by-step CUDA optimization walkthrough that improves an image-processing pipeline with debugging, benchmarking, optimized algorithms, GPU memory management, pinned transfers, streams, and asynchronous transfers. ### Source excerpt NVIDIA CUDA remains the foundation of GPU-accelerated computing, powering everything from scientific simulations to large-scale AI training. But writing... ## Co-Designing AI Models Using Speculative Decoding for Faster LLM Inference DevFeed: [Co-Designing AI Models Using Speculative Decoding for Faster LLM Inference]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-09-02T16:04:19Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Inference](), [Large Language Model]() Tags: [accelerate](), [agentic-ai-generative-ai](), [ai](), [ai-inference](), [ai-models](), [batch](), [data-center-cloud](), [developer-tools-techniques](), [how-to](), [inference](), [inference-performance](), [llm](), [llms](), [nvfp4](), [training-ai-models]() ### AI overview The article explains speculative decoding as a way to speed up LLM inference while preserving standard-decoding outputs. A smaller draft model proposes several tokens, which the larger target model verifies in parallel; it also defines draft and acceptance lengths and gives a speedup formula. ### Source excerpt This post is the third in a series on AI model co-design. It explores how to accelerate LLM inference while maintaining accuracy using speculative decoding and... ## Building an Adaptive Agentic Cybersecurity System with NVIDIA Nemotron DevFeed: [Building an Adaptive Agentic Cybersecurity System with NVIDIA Nemotron]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-09-01T17:00:04Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [AI Bots]() Tags: [agentic](), [agentic-ai-generative-ai](), [ai](), [attacks](), [cybersecurity](), [evaluation](), [models](), [nemo](), [nemotron](), [nvidia](), [open](), [orchestration](), [security-for-ai](), [telemetry](), [testing](), [top-stories](), [traces](), [trustworthy-ai-cybersecurity]() ### AI overview The article describes an agentic cybersecurity system that uses red and blue agents to continuously test attacks, analyze telemetry, generate detections, and retest them in an isolated representative environment. It discusses NVIDIA Nemotron models used with CrowdStrike SafeMind for defensive orchestration and detection generation. ### Source excerpt AI is changing the pace of cybersecurity. Agentic systems can coordinate work and pursue complex objectives over long horizons. Security teams are beginning to... ## How to Size GPUs for AI Inference and TCO Without Overspending DevFeed: [How to Size GPUs for AI Inference and TCO Without Overspending]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-09-01T15:00:00Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [AI Inference](), [GPU](), [Inference Performance](), [Large Language Model](), [cloud-infrastructure](), [Optimization]() Tags: [agentic-ai-generative-ai](), [ai-factory](), [ai-inference](), [concurrency](), [cost](), [data-center-cloud](), [gpu](), [inference-performance](), [latency](), [llm](), [mlops](), [quantization]() ### AI overview A practical guide to sizing GPU infrastructure for AI inference workloads while balancing latency, concurrency, model choice, deployment strategy, and total cost of ownership. ### Source excerpt The surge in AI adoption is transforming everything from chatbots to content generation. Still, a common pain point remains: How can organizations confidently... ## Run NVIDIA BioNeMo NIM Microservices for Protein Structure Prediction in Claude Science DevFeed: [Run NVIDIA BioNeMo NIM Microservices for Protein Structure Prediction in Claude Science]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-08-31T16:30:00Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [AI research agents](), [OpenSSH]() Tags: [agentic-ai](), [agentic-ai-generative-ai](), [bionemo](), [claude](), [code](), [developer-tools-techniques](), [drug-discovery](), [gpu](), [microservices](), [nim](), [nvidia](), [science](), [simulation-modeling-design](), [workflow]() ### AI overview A tutorial for running NVIDIA BioNeMo NIM microservices with Claude Science to perform protein-structure prediction using multiple-sequence alignment and multiple folding models. ### Source excerpt Agentic AI is changing how research is done. AI scientists can read papers, propose hypotheses, call models, and determine which experiments to prioritize next.... ## Scale AV Perception Across Vehicle Platforms with NVIDIA Omniverse NuRec DevFeed: [Scale AV Perception Across Vehicle Platforms with NVIDIA Omniverse NuRec]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-08-31T16:00:00Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Nvidia](), [synthetic-data](), [Simulation and Design](), [data](), [configuration]() Tags: [3d](), [autonomous](), [autonomous-vehicles](), [data](), [developer-tools-techniques](), [developers](), [development](), [model](), [models](), [nvidia](), [omniverse](), [performance](), [physical-ai](), [post](), [robotics](), [scale](), [simulation-modeling-design](), [synthetic-data](), [training](), [tutorial]() ### AI overview This tutorial explains how to adapt an autonomous-vehicle perception stack across carline and sensor-rig variants using existing real-world drives. It presents a four-step workflow with NVIDIA Omniverse NuRec: pair a reconstructed drive with a target rig, render target camera views, refine the frames with NVIDIA Harmonizer, and train a perception model on the output. ### Source excerpt A perception stack is shaped by the vehicle that carries it. Move the same software to a new carline--for example, from an SUV to a sedan or another vehicle... [Next page]()